## [1] "2024-04-15 11:37:58 CEST"
## [1] "explicated variable of regression : canopy_cover_rain_only_epsilon"
## [1] "for  all_Africa_regression_canopy_cover.RDS"                       
## [2] "for  Guinean_forest-savanna_regression_canopy_cover.RDS"           
## [3] "for  Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [4] "for  Sahelian_Acacia_savanna_regression_canopy_cover.RDS"          
## [5] "for  Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [6] "for  West_Sudanian_savanna_regression_canopy_cover.RDS"            
## [7] "for  Western_Congolian_forest-savanna_regression_canopy_cover.RDS" 
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "below, stancode for all_Africa_regression_canopy_cover.RDS"
## // generated with brms 2.20.4
## functions {
## }
## data {
##   int<lower=1> N;  // total number of observations
##   vector[N] Y;  // response variable
##   int<lower=1> K;  // number of population-level effects
##   matrix[N, K] X;  // population-level design matrix
##   int<lower=1> Kc;  // number of population-level effects after centering
##   int prior_only;  // should the likelihood be ignored?
## }
## transformed data {
##   matrix[N, Kc] Xc;  // centered version of X without an intercept
##   vector[Kc] means_X;  // column means of X before centering
##   for (i in 2:K) {
##     means_X[i - 1] = mean(X[, i]);
##     Xc[, i - 1] = X[, i] - means_X[i - 1];
##   }
## }
## parameters {
##   vector[Kc] b;  // regression coefficients
##   real Intercept;  // temporary intercept for centered predictors
##   real<lower=0> phi;  // precision parameter
## }
## transformed parameters {
##   real lprior = 0;  // prior contributions to the log posterior
##   lprior += student_t_lpdf(Intercept | 3, 0, 2.5);
##   lprior += gamma_lpdf(phi | 0.01, 0.01);
## }
## model {
##   // likelihood including constants
##   if (!prior_only) {
##     // initialize linear predictor term
##     vector[N] mu = rep_vector(0.0, N);
##     mu += Intercept + Xc * b;
##     mu = inv_logit(mu);
##     target += beta_lpdf(Y | mu * phi, (1 - mu) * phi);
##   }
##   // priors including constants
##   target += lprior;
## }
## generated quantities {
##   // actual population-level intercept
##   real b_Intercept = Intercept - dot_product(means_X, b);
## }
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] " "
## [1] " "
## [1] "all_Africa_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 13362) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -3.03      0.01    -3.06    -3.01 1.00     2413     2409
## mean_precip_std     0.41      0.01     0.40     0.43 1.00     2426     2290
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     7.92      0.12     7.68     8.16 1.00     2548     2167
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Guinean_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 1725) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -2.12      0.06    -2.24    -2.01 1.00     2515     2500
## mean_precip_std     0.13      0.03     0.08     0.18 1.00     2546     2330
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     4.34      0.16     4.04     4.65 1.00     2414     2410
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 243) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -1.13      0.36    -1.82    -0.41 1.00     2475     2351
## mean_precip_std    -0.23      0.17    -0.56     0.08 1.00     2426     2371
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     5.46      0.50     4.52     6.49 1.00     2032     2455
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 5563) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -4.62      0.02    -4.67    -4.58 1.00     2502     2204
## mean_precip_std     0.84      0.03     0.78     0.90 1.00     2303     2219
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi    85.14      2.04    81.07    89.10 1.00     2267     2239
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 47) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -1.42      1.31    -4.00     1.04 1.00     2445     2233
## mean_precip_std    -0.71      0.65    -1.94     0.58 1.00     2404     2397
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     5.61      1.59     3.03     9.24 1.00     2248     2151
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "West_Sudanian_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 3277) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -3.60      0.04    -3.68    -3.52 1.00     2390     2212
## mean_precip_std     0.86      0.03     0.79     0.92 1.00     2264     2165
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     9.43      0.28     8.91     9.99 1.00     2621     2370
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: beta 
##   Links: mu = logit; phi = identity 
## Formula: canopy_cover ~ mean_precip_std 
##    Data: table_region (Number of observations: 259) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -2.25      0.23    -2.70    -1.78 1.00     2441     2332
## mean_precip_std     0.13      0.15    -0.17     0.42 1.00     2502     2342
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     3.23      0.33     2.63     3.91 1.00     2626     2427
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Beta regressions for  all_Africa_regression_canopy_cover.RDS"                       
## [2] "Beta regressions for  Guinean_forest-savanna_regression_canopy_cover.RDS"           
## [3] "Beta regressions for  Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [4] "Beta regressions for  Sahelian_Acacia_savanna_regression_canopy_cover.RDS"          
## [5] "Beta regressions for  Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [6] "Beta regressions for  West_Sudanian_savanna_regression_canopy_cover.RDS"            
## [7] "Beta regressions for  Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "all_Africa_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 13362  2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## Le chargement a nécessité le package : gtools
## 
## Attachement du package : 'gtools'
## Les objets suivants sont masqués depuis 'package:brms':
## 
##     ddirichlet, rdirichlet

## [1] "mean(table_region$canopy_cover)"
## [1] 0.05362057
## [1] "sd(table_region$canopy_cover)"
## [1] 0.09756511

## [1] "Guinean_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 1725 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.1324814
## [1] "sd(table_region$canopy_cover)"
## [1] 0.14466

## [1] "Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]  243 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.1659144
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1334132

## [1] "Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 5563 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.006161202
## [1] "sd(table_region$canopy_cover)"
## [1] 0.02202149

## [1] "Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]   47 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.05084602
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1063901

## [1] "West_Sudanian_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 3277 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.05400694
## [1] "sd(table_region$canopy_cover)"
## [1] 0.08641946

## [1] "Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]  259 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.1089882
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1649516